Tag Archives: credit history

Confusion Reigns on Predictive Analytics

It seems everyone in workers’ compensation wants analytics. At the same time, a lot of confusion persists about what analytics is and what it can contribute. Expectations are sometimes unclear and often unrealistic. Part of the confusion is that analytics can exist in many forms.

Analytics is a term that encompasses a broad range of data mining and analysis activities. The most common form of analytics is straightforward data analysis and reporting. Other predominant forms are predictive modeling and predictive analytics.

Most people are already doing at least some form of analytics and portraying their results for their unique audiences. Analytics represented by graphic presentations are popular and often informative, but they do not change behavior and outcomes by themselves.

See Also: Analytics and Survival in the Data Age

Predictive modeling uses advanced mathematical tools such as various configurations of regression analysis or even more esoteric mathematical instruments. Predictive modeling looks for statistically valid probabilities about what the future holds within a given framework. In workers’ compensation, predictive modeling is used to forecast which claims will be the most problematic and costly from the outset of the claim. It is also the most sophisticated and usually the most costly predictive methodology.

Predictive analytics lies somewhere between data analysis and predictive modeling. It can be distinguished from predictive modeling in that it uses historic data to learn from experience what to expect in the future. It is based on the assumption that future behavior of an individual or situation will be similar to what has occurred in the past.

One of the best-known applications of predictive analytics is credit scoring, used throughout the financial services industry. Analysis of a customer’s credit history, payment history, loan application and other conditions is used to rank-order individuals by their likelihood of making future credit payments on time. Those with the highest scores are ranked highest and are the best risks. That is why a high credit risk score is important to purchasers and borrowers.

Similarly, workers’ compensation claim data can be collected, integrated and analyzed from bill review, claims system, utilization review, pharmacy (PBM) and claim outcome information to score and rank-order treating physicians’ performance. Those with the highest rank are the most likely to move the injured worker to recovery more quickly and at the lowest cost.

Both predictive modeling and predictive analytics deal in probabilities regarding future behavior. Predictive modeling uses statistical methods, and predictive analytics looks at what was, is and, therefore, probably will be. For predictive analytics, it is important to identify relevant variables that can be found in the data and take action when those conditions or events occur in claims.

One way to find critical variables is to review industry research. For instance, research has shown that, when there is a gap between the date of injury and reporting or the first medical treatment, something is not right. That gap is an outlier in the data that predicts claim complexity.

Another way to identify key variables is to search the data to find the most costly cases and then look for consistent variables among them. Each book of business may have unique characteristics that can be identified in that manner.

Importantly, predictive analytics can be used concurrently throughout the course of the claim. The data is monitored electronically to continually search for outlier variables. When predictive outliers occur in the data, alerts can be sent to the appropriate person so that interventions are timely and more effective.

For example, to evaluate medical provider future performance, select data elements that describe past behavior. Look at past return-to-work patterns and indemnity costs associated with providers. If a provider has not typically returned injured workers to work in the past, chances are pretty good that behavior will continue.

For organizations looking to implement analytics, those who have already made the plunge suggest starting by taking stock of your organization’s current state. “The first thing you need to know is what is happening in your population,” says Rishi Sikka, M.D., senior vice president of clinical transformation for Advocate Health Care in Illinois. “Everyone wants to do all the sexy models and advanced analytics, but just understanding that current state, what is happening, is the first and the most important challenge.”

The accuracy and usability of results will depend greatly on the quality of the data analyzed. To get the best and most satisfying results from predictive analytics, cleanse the data by removing duplicate entries, data omissions and inaccuracies.

For powerful medical management informed by analytics, identify the variables that are most problematic for the organization and continually scan the data to find claims that contain them. Then send an alert. Structuring the outliers, monitoring the data to uncover claims containing them, alerting the right person and taking the right action is a powerful medical management strategy.

When Are Background Checks Not Allowed?

The Equal Employment Opportunity Commission (EEOC) has been quite active in challenging employers’ use of criminal background and credit history checks during hiring. There is still significant uncertainty as to the current standards and law about the checks of criminal and credit history. The lack solid guidance makes it difficult for employers to determine how to evaluate their current use of this information, as well as to understand the legal pitfalls and hurdles that the EEOC has placed in front of them.

EEOC Directives

The recent activity emanates from the EEOC’s recent directive and key priority (as per its December 2012 Strategic Enforcement Plan (SEP)) to eliminate hiring barriers. This priority includes challenges to policies and practices that exclude applicants based on criminal history or credit check. The EEOC has a keen interest in this area, as it believes that criminal/credit checks have a disparate impact on African American and Hispanic applicants. As the EEOC pursues the directive, expect the EEOC to scrutinize failure-to-hire claims where a criminal history or background check was conducted. Even if the background check was “facially neutral” and was uniformly given to all applicants, the EEOC may investigate to determine if the check had a “discriminatory effect” on certain applicant(s).

The EEOC asserts that criminal background checks must be “job-related” and “consistent with business necessity.” Employers are advised to consider: (1) the nature and gravity of the offense or conduct; (2) the time that has passed since the offense, conduct or completion of the sentence; and (3) the nature of the job held or sought. The EEOC stresses the need for an “individualized assessment” before excluding an applicant based on a criminal or credit record.

Local/State/Federal Laws

Employers face additional legal hurdles regarding hiring practices because of recent local and state legislative developments. These laws are commonly referred to as “ban the box” (i.e., restrictions on the use of criminal history in hiring and employment decisions). Making matters even more difficult, employers have also been subject to a surge in class action litigation under the Fair Credit Reporting Act (FCRA). The FCRA regulates the use of and gathering of criminal histories through third-party consumer reporting agencies with respect to conducting background checks on applicants or employees.

Legal Actions

In pursuit of its directive, the EEOC has filed several large-scale lawsuits against employers. We expect that the EEOC will continue to file similar lawsuits throughout 2015 and beyond. Most have been brought as failure-to-hire claims. For example, an African-American woman brought a claim alleging that she was discriminated against based on her credit history. This claim started out as a single plaintiff action, but, after the EEOC conducted its initial investigation, the EEOC dramatically expanded the scope of the initial charge, alleging that the employer was engaging in a “pattern and practice of unlawful discrimination” against: (1) African-American applicants by using poor credit history as a hiring criterion and (2) African-American, Hispanic and white male applicants by using criminal history as a hiring criterion.

Reasonable employers complain that the EEOC has placed employers in a Catch 22. Employers have to choose between ignoring criminal history and credit background, exposing themselves to potential liability for criminal and fraudulent acts committed by employees or to an EEOC lawsuit for having used this information in a discriminatory way.

Takeaway for Employers

Claims involving criminal background checks and credit checks are an EEOC priority. At this time, employers have little guidance from the courts or the EEOC as to exactly what “job-related” and “consistent with business necessity” mean and just how closely a past criminal conviction has to correspond with the duties of a particular job for an employer to legally deny employment to an applicant. Moreover, employers continue to witness expanding restrictions dealing with criminal history at the state and local level based on ban-the-box legislation, as well as with an increasing number of class action lawsuits involving background checks as required under the Fair Credit Reporting Act.

Employers are encouraged to work closely with legal counsel as to what they should and should not ask on applicants as well as how and when they can use background information they obtain. Based on this evolving area of the law, we additionally recommend that employers purchase a robust EPL policy that will defend them in the event that the EEOC or a well-skilled plaintiff’s counsel pursues a claim against them for discrimination, or for failure to hire based on criminal or credit background checks.